Image processing and deep learning for colorectal cancer diagnosis

Abstract

Artificial Intelligence, smart applications, machine learning, and the Internet of Things, are now revolutionizing virtually every aspect of our lives. Healthcare applications are currently one of the most promising areas for machine learning specialists, with emerging approaches that aim to modernize medical practice. This thesis aims to introduce readers to the latest technological advances in the field of deep learning, and more specifically its applications in the diagnosis and surgical management of colorectal cancer. Colorectal cancer is used as a template condition throughout the book, largely due to its nature as one of the most well-studied types of malignancy; it offers a unique combination of high prevalence and radical treatment potential through surgical approaches. The applications presented here, include studied and tested methods, many of which have already found their way into everyday surgical practice. This book aims to be a valuable resource for all healthcare researchers, AI-specialized computer scientists, surgical trainees, and clinicians alike. We have strived to outline the latest and most promising advances of AI in surgical practice while emphasizing why physicians still have to play a central, pivotal role in rolling out such advances. AI is expected to define the future of personalized healthcare delivery and enhance patient safety and satisfaction.